Image analysis method and image analysis system

ABSTRACT

The image analysis method of the disclosure includes following steps: obtaining an image of a multi-layer structure provided by an electron microscope and displaying the image of the multi-layer structure through a display device, wherein the image of the multi-layer structure is a gray-scale image; setting a measurement line segment on the image of the multi-layer structure, wherein the measurement line segment extends along a first direction; detecting a gray-scale distribution within the measurement line segment corresponding to the image of the multi-layer structure along the measurement line segment; and analyzing the gray-scale distribution to determine a plurality of dark layer thicknesses and a plurality of light layer thicknesses according to a threshold range.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority benefit of U.S. application Ser.No. 63/113,173, filed on Nov. 12, 2020, and Taiwan application serialno. 110124775, filed on Jul. 6, 2021. The entirety of each of theabove-mentioned patent applications is hereby incorporated by referenceherein and made a part of this specification.

BACKGROUND Technical Field

The disclosure relates to an analysis method, and in particular, to animage analysis method and an image analysis system.

Description of Related Art

In a semiconductor manufacturing process, the size of a device mayaffect the electrical change. Therefore, it is necessary for the size ofa device to be accurate. An electron microscope with a magnifyingfunction, such as a scanning electron microscope (SEM) or a transmissionelectron microscope (TEM), is usually used in the measurement of asemiconductor device. However, when using an electron microscope tomeasure the size of a device image, the size of each region is obtainedby manually setting an edge point of each region one by one, which takesmore time. Accordingly, some embodiments below are proposed as solutionsto the issue above.

SUMMARY

The disclosure is directed to an image analysis method and an imageanalysis system which may automatically measure a thickness of eachlayer of an image of a multi-layer structure according to a setmeasurement line segment.

The image analysis method of the disclosure includes the following. Animage of a multi-layer structure provided by an electron microscope isobtained. The image of the multi-layer structure is displayed through adisplay device, and the image of the multi-layer structure is agray-scale image. A measurement line segment is set on the image of themulti-layer structure, and the measurement line segment extends along afirst direction. A gray-scale distribution within the measurement linesegment corresponding to the image of the multi-layer structure isdetected along the measurement line segment. The gray-scale distributionis analyzed to determine multiple dark layer thicknesses and multiplelight layer thicknesses in the image of the multi-layer structureaccording to a threshold range.

The image analysis system of the disclosure includes an electronmicroscope, a display device, and an image analysis device. The electronmicroscope is configured to provide an image of a multi-layer structure.The display device is configured to display the image of the multi-layerstructure. The image analysis device is coupled to the electronmicroscope and the display device to obtain the image of the multi-layerstructure provided by the electron microscope and output the image ofthe multi-layer structure to the display device. The image analysisdevice includes a storage device and a processor. The storage deviceincludes an image analysis module. The processor is coupled to thestorage device. The processor inputs the image of the multi-layerstructure into the image analysis module. The processor sets ameasurement line segment on the image of the multi-layer structure, andthe measurement line segment extends along a first direction. Theprocessor detects a gray-scale distribution within the measurement linesegment corresponding to the image of the multi-layer structure alongthe measurement line segment through the image analysis module. Theprocessor analyzes the gray-scale distribution through the imageanalysis module to determine multiple dark layer thicknesses andmultiple light layer thicknesses in the image of the multi-layerstructure according to a threshold range.

Based on the above, the image analysis method and the image analysissystem of the disclosure may automatically measure the thickness of eachlayer of the image of the multi-layer structure according to the setmeasurement line segment. Therefore, a great amount of time spent onmanual operation is reduced.

In order to make the aforementioned features and advantages of thedisclosure comprehensible, embodiments accompanied with drawings aredescribed in detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an image analysis system according toan embodiment of the disclosure.

FIG. 2 is a flow chart of an image analysis method according to anembodiment of the disclosure.

FIG. 3 is a schematic diagram of an image of a multi-layer structureaccording to an embodiment of the disclosure.

FIG. 4 is a schematic diagram of a gray-scale distribution according toan embodiment of the disclosure.

FIG. 5 is a schematic diagram of a display interface according to anembodiment of the disclosure.

FIG. 6 is a flow chart of an image analysis method according to anembodiment of the disclosure.

FIG. 7 is a schematic diagram of a gray-scale distribution of an imageof a multi-layer structure according to an embodiment of the disclosure.

DESCRIPTION OF THE EMBODIMENTS

In order to make the contents of the disclosure easier to understand,the following embodiments are specifically described as examples basedon which the disclosure may be implemented. Wherever possible, theelements/components/steps with the same reference numerals in thedrawings and embodiments represent the same or similar parts.

In addition, unless otherwise defined, all terms (including technicaland scientific terms) used herein have the same meaning as commonlyunderstood by those of ordinary skill in the art to which the disclosurebelongs. It will be further understood that terms such as those definedin commonly used dictionaries shall be construed to have a meaningconsistent with their meaning in the context of of the relevant art andthe disclosure and will not be construed to have an idealized or overlyformal meaning unless expressly defined as such herein.

FIG. 1 is a schematic diagram of an image analysis system according toan embodiment of the disclosure. Referring to FIG. 1, an image analysissystem 100 may include an image analysis device 101, an electronmicroscope 140, and a display device 150. The electron microscope 140may be configured to provide an image of a multi-layer structure byphotographing an object of a semiconductor manufacturing process (asemiconductor product). The image of the multi-layer structure is anelectron microscope image and a gray-scale image. The image of themulti-layer structure may include a multi-layer semiconductor structurelayer of different materials, and a gray-scale distribution of an imageof the multi-layer semiconductor structure layer may be determinedaccording to different semiconductor materials.

In the embodiment, the display device 150 may be configured to displaythe image of the multi-layer structure. The image analysis device 101may be coupled to the electron microscope 140 and the display device 150to obtain the image of the multi-layer structure provided by theelectron microscope 140 and output the image of the multi-layerstructure to the display device 150. The image analysis device 101 mayinclude a processor 110 and a storage device 120. The storage device 120may include an image analysis module 121. The processor 110 may becoupled to the storage device 120. In the embodiment, the image analysisdevice 101 may be an independent computer device or cloud server. Thedisclosure is not limited thereto.

In the embodiment, the processor 110 may input the image of themulti-layer structure into the image analysis module 121, and theprocessor 110 may set a measurement line segment on the image of themulti-layer structure. The measurement line segment extends along adirection in which the multi-layer structure is stacked. In theembodiment, a method of the processor 110 setting the measurement linesegment may include manual setting or automatic setting. Here, manualsetting may be, for example but not limited to, setting a position ofthe measurement line segment through a setting instruction or parameter(e.g. input by a user) provided by an input device of the image analysissystem 100. Automatic setting may be, for example but not limited to,automatic setting according to a margin range of an image or automaticsetting according to a predetermined condition by the image analysissystem 100.

Next, the processor 110 may detect a gray-scale distribution within themeasurement line segment corresponding to the image of the multi-layerstructure along the measurement line segment through the image analysismodule 121. Furthermore, the processor 110 may analyze the gray-scaledistribution through the image analysis module 121 to determine multipledark layer thicknesses and multiple light layer thicknesses in the imageof the multi-layer structure according to a threshold range. In thisway, the image analysis system 100 may automatically measure thethickness of each layer of the image of the multi-layer structureaccording to the set measurement line segment. Therefore, a great amountof time spent on manual operation is reduced.

In the embodiment, the processor 110 may be, for example but not limitedto, a central processing unit (CPU), a microprocessor control unit(MCU), or a field programmable gate array (FPGA).

In the embodiment, the storage device 120 may be, for example but notlimited to, a random access memory (RAM), a read-only memory (ROM), anoptical disc, a magnetic disk, a hard drive, a solid-state drive, aflash drive, a security digital (SD) card, a memory stick, a compactflash (CF) card, or any types of storage devices. The storage device 120may store the image analysis module 121 and the related image data, therelated analysis result and data, the display interface, and the likedescribed in each embodiment for the processor 110 to access andexecute.

In the embodiment, the electron microscope 140 may be, for example butnot limited to, a scanning electron microscope (SEM) or a transmissionelectron microscope (TEM). In the embodiment, the display device 150 maybe, for example, various electronic devices having a display function.In addition, in another embodiment, the display device 150 may bedisposed in the image analysis device 101 so that the image analysisdevice 101 may be, for example, a computer device having a displayfunction.

FIG. 2 is a flow chart of an image analysis method according to anembodiment of the disclosure. FIG. 3 is a schematic diagram of an imageof a multi-layer structure according to an embodiment of the disclosure.FIG. 4 is a schematic diagram of a gray-scale distribution according toan embodiment of the disclosure. Referring to FIG. 1 to FIG. 4, in theembodiment, the image analysis system 100 may execute the followingsteps S210 to S250 of an image analysis method. In step S210, the imageanalysis device 101 may obtain an image 300 of a multi-layer structureprovided by the electron microscope 140. In step S220, the imageanalysis device 101 may display the image 300 of the multi-layerstructure through the display device 150. In step S230, the imageanalysis module 121 may set a measurement line segment 330 on the image300 of the multi-layer structure. In step S240, the image analysismodule 121 detects a gray-scale distribution 400 within the measurementline segment 330 corresponding to the image 300 of the multi-layerstructure along the measurement line segment 330. In step S250, theimage analysis module 121 analyzes the gray-scale distribution 400 todetermine multiple dark layer thicknesses and multiple light layerthicknesses in the image 300 of the multi-layer structure according to athreshold range 461 and a threshold range 462.

Specifically, the image 300 of the multi-layer structure may be agray-scale image, and a first direction P1 is perpendicular to a seconddirection P2. Furthermore, the image 300 of the multi-layer structuremay include multiple dark layer images 310-1 to 310-4 and multiple lightlayer images 320-1 to 320-5. The dark layer images 310-1 to 310-4 andthe light layer images 320-1 to 320-5 are arranged in an interlacingmanner along the first direction P1, and the dark layer images 310-1 to310-4 and the light layer images 320-1 to 320-5 respectively extendalong the second direction P2. In the embodiment, the dark layer images310-1 to 310-4 may be a first type semiconductor material layer, and thelight layer images 320-1 to 320-5 may be a second type semiconductormaterial layer. The first type semiconductor material layer is differentfrom the second type semiconductor material layer. In the embodiment,multiple white thin layer images 340-1 to 340-7 may be further presentrespectively between the dark layer images 310-1 to 310-4 and the lightlayer images 320-1 to 310-5, and the white thin layer images 340-1 to340-7 may be a third type semiconductor material layer which isdifferent from the first type and the second type.

Referring to FIG. 1 to FIG. 4, in step S230, the processor 110 mayexecute the image analysis module 121 to set the measurement linesegment 330 on the image 300 of the multi-layer structure (throughmanual setting or automatic setting as described above). In step S240,the processor 110 may detect multiple consecutive pixels within themeasurement line segment 330 corresponding to the image 300 of themulti-layer structure along the measurement line segment 330 to obtainmultiple gray-scale values of the pixels. In addition, the processor 110may establish the gray-scale distribution 400 as shown in FIG. 4according to the gray-scale values. In step S250, the processor 110 mayanalyze the gray-scale distribution 400 and determine the dark layerthicknesses and the light layer thicknesses in the image 300 of themulti-layer structure according to the predetermined threshold range 461and the predetermined threshold range 462. Here, as shown in FIG. 4, avalue on the horizontal axis of the gray-scale distribution 400 maycorrespond to each of the pixels on the measurement line segment 330along the first direction P1, and a value on the vertical axis mayrepresent a gray-scale value of each of the pixels within themeasurement line segment 330 corresponding to the image 300 of themulti-layer structure.

In the embodiment, the threshold range 461 (also referred to as a firstthreshold range) may be set as, for example but not limited to, 0 to 15,and the threshold range 462 (also referred to as a second thresholdrange) may be set as, for example but not limited to, 45 to 90. That is,when a gray-scale value of a pixel is between 0 and 15, the pixel may beconsidered to be a dark pixel. Conversely, when a gray-scale value ofanother pixel is between 45 and 90, the pixel may be considered to be alight pixel. For example, in step S250, the processor 110 may determinea pixel whose gray-scale value is in the threshold range 461 as the darkpixel. Further, the processor 110 may determine a pixel whose gray-scalevalue is in the threshold range 462 as the light pixel. Next, theprocessor 110 may calculate the number of dark pixels corresponding toeach of the dark layer thicknesses or the number of light pixelscorresponding to each of the light layer thicknesses on the measurementline segment 330 by determination of the starting point and the endpoint of each layer according to a slope value of two consecutivegray-scale values to respectively obtain each of the dark layerthicknesses or each of the light layer thicknesses. That is, each of thedark layer thicknesses corresponds to the number of the dark pixels ofeach of the dark layer thicknesses, and each of the light layerthicknesses corresponds to the number of the light pixels of each of thelight layer thicknesses.

In the embodiment, when the processor 110 finishes calculating thenumber of the dark pixels of each of the dark layers and the number ofthe light pixels of each of the light layers, the processor 110 mayinstantly convert such numbers into corresponding thickness parametersto output a measurement result. For example, a pixel may correspond to 1nanometer. In an embodiment, assuming that the sixth pixel to theforty-fourth pixels are the light pixels, the processor 110 may obtainthat a thickness of a layer of a corresponding light layer image is 39nanometers (44-6+1=39). However, a correspondence relationship between apixel and a length may be adjusted through manual setting or automaticdetection. The disclosure is not limited thereto.

In addition, in the embodiment, the overall measurement result may bepresented as Table 1 below and displayed on the display device 150. Thedisclosure is not limited thereto. In the embodiment, layer numbers mayrepresent numbers of the dark layer images 310-1 to 310-4 or the lightlayer images 320-1 to 320-5 which sequentially intersect with themeasurement line segment 330 along the first direction P1 in the image300 of the multi-layer structure. In the embodiment, an averagegray-scale value may represent an average value of the gray-scale valuesof the pixels corresponding to the dark layer images 310-1 to 310-4 orthe light layer images 320-1 to 320-5 which sequentially intersect withthe measurement line segment 330 along the first direction P1 in theimage 300 of the multi-layer structure. In the embodiment, a measurementthickness may represent a dark layer thickness or a light layerthickness corresponding to the layer number.

TABLE 1 Layer number Average gray-scale value Measurement thickness 1 838 2 80 39 3 5 38 4 83 35

In addition, in an embodiment, the image 300 of the multi-layerstructure may include the dark layer images 310-1 to 310-4 and the lightlayer images 320-1 to 320-5 with multiple different gray-scale valueranges. The processor 110 may correspondingly set the threshold range461, the threshold range 462, or other threshold ranges to measure thethicknesses. Furthermore, in an embodiment, in the image 300 of themulti-layer structure, the dark layer images 300-1 to 310-4 and thelight layer images 320-1 to 320-5 with the different gray-scale valueranges may be arranged randomly along the first direction P1, but notlimited to the dark layer images 310-1 to 310-4 and the light layerimages 320-1 to 320-5 being arranged in an interlacing manner along thefirst direction P1.

FIG. 5 is a schematic diagram of a display interface according to anembodiment of the disclosure. Referring to FIG. 3 and FIG. 5, a displayinterface 500 may include an image 510 of a multi-layer structure, atool bar 520, and a measurement result 530. With regard to thedescription of the image 510 of the multi-layer structure, thedescription of the image 300 of the multi-layer structure in FIG. 3 maybe referred to, and it will not be repeated here. In the embodiment, thetool bar 520 may include buttons of a magnifier, moving a display range,setting a measurement line segment, and the like to be configured for auser to operate; however, the disclosure is not limited thereto. Forexample, the user may use the button of setting a measurement linesegment on the tool bar 520 to set the desired measurement line segment330 on his own. Furthermore, the measurement result 530 may include, forexample, the layer number and the dark layer thickness corresponding tothe dark layer (Dark) or the light layer thickness corresponding to thelight layer. Note that, in the measurement result 530, the dark layerthickness and the light layer thickness respectively correspond to asame layer number. In another embodiment, the measurement result 530 maybe in a form like Table 1, and the layer number only corresponds to onedark layer thickness or one light layer thickness. In the embodiment,the display interface 500 may be displayed on the display device 150 forthe user to instantly view the measurement result 530.

FIG. 6 is a flow chart of an image analysis method according to anembodiment of the disclosure. Referring to FIG. 1 to FIG. 4 and FIG. 6,step S250 in FIG. 2 may be realized, for example, by adopting the methodin FIG. 6. In the embodiment, when the processor 110 executes the imageanalysis module 121 to analyze the gray-scale distribution 400, theprocess may be divided into the four steps below: step S610 (finding astarting point of the dark layer thickness or the light layerthickness), step S620 (finding an endpoint of the dark layer thicknessor the light layer thickness), step S630 (checking a thickness range),and step S640 (checking whether the thicknesses are overlapped).

Note that since gray-scale values of the white thin layer images 340-1to 340-7 are greater than the gray-scale values of the dark layer images310-1 to 310-4 and the light layer images 320-1 to 320-5, when theprocessor 110 analyzes the gray-scale distribution 400, the gray-scalevalues decrease gradually in both cases of entering from the white thinlayer images 340-1 to 340-7 to the dark layer images 310-1 to 310-4 orentering from the white thin layer images 340-1 to 340-7 to the lightlayer images 320-1 to 320-5. That is, a slope value of two consecutivegray-scale values is negative. Next, as long as the processor 110determines that an interval formed by two consecutive gray-scale valuesincludes upper limit values of the threshold range 461 and the thresholdrange 462 and whether the second gray-scale value of the two consecutivegray-scale values belongs to the threshold range 461 of the dark layerimages 310-1 to 310-4 or the threshold range 462 of the light layerimages 320-1 to 320-5, the processor 110 may mark the starting point ofthe dark layer thickness or the light layer thickness. That is, when theprocessor 110 determines that an interval formed by two consecutivegray-scale values includes the upper limit value of the threshold range461 (also referred to as a first upper limit value) and that the secondpixel of the corresponding two consecutive pixels is the dark pixel, theprocessor 110 marks the second pixel of the corresponding twoconsecutive pixels as the dark layer starting point of the correspondingdark layer thickness. Similarly, when the processor 110 determines thatan interval formed by two consecutive gray-scale values includes theupper limit value of the threshold range 462 (also referred to as asecond upper limit value) and that the second pixel of the correspondingtwo consecutive pixels is the light pixel, the processor 110 marks thesecond pixel of the corresponding two consecutive pixels as the lightlayer starting point of the corresponding light layer thickness.

In addition, when the processor 110 analyzes the gray-scale distribution400, the gray-scale values increase gradually in both cases of enteringfrom the dark layer images 310-1 to 310-4 into the white thin layerimages 340-1 to 340-7 or entering from the light layer images 320-1 to320-5 to the white thin layer images 340-1 to 340-7. That is, a slopevalue of two consecutive gray-scale values is positive. Next, as long asthe processor 110 determines that an interval formed by two consecutivegray-scale values includes the upper limit values of the threshold range461 and the threshold range 462 and whether the second gray-scale valueof the two consecutive gray-scale values does not belong to thethreshold range 461 of the dark layer images 310-1 to 310-4 or thethreshold range 462 of the light layer images 320-1 to 320-5, theprocessor 110 may mark the endpoint of the dark layer thickness or thelight layer thickness. That is, when the processor 110 determines thatan interval formed by two consecutive gray-scale values includes thefirst upper limit value of the threshold range 461 and that the firstpixel of the corresponding two consecutive pixels is the dark pixel, theprocessor 110 marks the first pixel of the corresponding two consecutivepixels as the dark layer endpoint of the corresponding dark layerthickness. Similarly, when the processor 110 determines that an intervalformed by two consecutive gray-scale values includes the second upperlimit value of the threshold range 462 and that the first pixel of thecorresponding two consecutive pixels is the light pixel, the processor110 marks the first pixel of the corresponding two consecutive pixels asthe light layer endpoint of the corresponding light layer thickness.

In the embodiment, the processor 110 may set a decreasing thresholdvalue and determine whether the slope value is less than the decreasingthreshold value to mark the starting point of the dark layer thicknessor the light layer thickness. In the embodiment, the processor 110 mayset an increasing threshold value and determine whether the slope valueis greater than the increasing threshold value to mark the endpoint ofthe dark layer thickness or the light layer thickness. For example, bothof the decreasing threshold value and the increasing threshold value maybe set as 0; however, the disclosure is not limited thereto. That is,the processor 110 may determine whether the slope value is negative(less than 0) to mark the starting point of the dark layer thickness orthe light layer thickness. Furthermore, the processor 110 may determinewhether the slope value is positive (greater than 0) to mark theendpoint of the dark layer thickness or the light layer thickness. In anembodiment, the decreasing threshold value and the increasing thresholdvalue may be respectively set as the same value or different valuesaccording to a need of design, and the disclosure is not limitedthereto.

For example, in step S610, when the processor 110 analyzes thegray-scale distribution 400, the processor 110 may calculate the slopevalue of two consecutive gray-scale values. The processor 110 may markthe pixel corresponding to the second gray-scale value of the twoconsecutive gray-scale values as the starting point of the dark layer orthe light layer according to a change of the slope value and bydetermining whether the second gray-scale value of the two consecutivegray-scale values falls into the threshold range 461 corresponding tothe dark layer images 310-1 to 310-4 or the threshold range 462corresponding to the light layer images 320-1 to 320-5. That is, theprocessor 110 determines whether the interval formed by the twoconsecutive gray-scale values includes the upper limit values of thethreshold range 461 and the threshold value 462 to mark the pixelcorresponding to the second gray-scale value of the two consecutivegray-scale values as the starting point of the dark layer or the lightlayer.

In step S620, when the processor 110 analyzes the gray-scaledistribution, the processor 110 may calculate the slope value of twoconsecutive gray-scale values. The processor 110 may mark the pixelcorresponding to the first gray-scale value of the two consecutivegray-scale values as the endpoint of the dark layer or the light layeraccording to the change of the slope value and by determining whetherthe second gray-scale value of the two consecutive gray-scale valuesleaves the threshold range 461 corresponding to the dark layer images310-1 to 310-4 or the threshold range 462 corresponding to the lightlayer images 320-1 to 320-5. That is, the processor 110 determineswhether the interval formed by the two consecutive gray-scale valuesincludes the upper limit values of the threshold range 461 and thethreshold value 462 to mark the pixel corresponding to the firstgray-scale value of the two consecutive gray-scale values as theendpoint of the dark layer or the light layer.

Next, in step S630, the processor 110 respectively calculates the numberof the pixels between the starting point and the endpoint correspondingto each of the dark layer thicknesses or light layer thicknesses. Inthis way, the processor 110 may calculate the corresponding dark layerthicknesses or light layer thicknesses according to the number of thepixels between the starting point and the endpoint corresponding to eachof the dark layer thicknesses or light layer thicknesses.

Furthermore, the processor 110 may check the pixels to leave out anincorrect starting point. For example, the processor 110 may checkwhether the average gray-scale values of all the pixels between thestarting point and the endpoint of each of the dark layer thicknessesfall into the threshold range 461 corresponding to the dark layer images310-1 to 310-4 or whether the average gray-scale values of all thepixels between the starting point and the endpoint of each of the lightlayer thicknesses fall into the threshold range 462 corresponding to thelight layer images 320-1 to 320-5. When the checking result is correct,the processor 110 may calculate the number of the pixels between thestarting point and the endpoint corresponding to each of the dark layerthicknesses or light layer thicknesses. Conversely, when the checkingresult is incorrect, the processor 110 may leave out a current startingpoint and start over to continue determining a next starting point as anew starting point.

Last, in step S640, the processor 110 may check whether the adjacentdark layer thickness or the adjacent light layer thickness correspondsto the same pixel to check whether the thicknesses are overlapped.Overlapped thicknesses are considered to be the same dark layerthickness or light layer thickness. That is, when the processor 110checks that the adjacent dark layer thickness or the adjacent lightlayer thickness corresponds to the same pixel, the processor 110 maycombine the adjacent light layer thickness or the adjacent dark layerthickness into a single light layer thickness or dark layer thickness.In this way, the processor 110 may correct an overlap of the dark layerthickness or the light layer thickness caused by an interference ofnoise so as to obtain a correct dark layer thickness or light layerthickness. In addition, in an embodiment, step S640 may be omitted, andthe thickness calculated and obtained in step S630 may be directly takenas the dark layer thickness or the light layer thickness.

FIG. 7 is a schematic diagram of a gray-scale distribution of an imageof a multi-layer structure according to an embodiment of the disclosure.Referring to FIG. 1 and FIG. 7, in the embodiment, a gray-scaledistribution 700 of an image of a multi-layer structure may only includemultiple dark layer images 710-1 to 710-4 and multiple light layerimages 720-1 to 720-3 and may omit the white thin layer images 340-1 to340-7 as shown in FIG. 3. Similar to the method described above, theprocessor 110 may respectively determine whether two consecutivegray-scale values belong to a threshold range 761 of the dark layerimages 710-1 to 710-4 and a threshold range 762 of the light layerimages 720-1 to 720-3 when determining the starting point or theendpoint of the dark layer thickness or the light layer thickness. Inother words, the processor 110 may determine whether an interval formedby two consecutive gray-scale values includes an upper limit value ofthe threshold range 761 to mark the starting point or the endpoint ofthe dark layer thickness. In addition, the processor 110 may determinewhether an interval formed by two consecutive gray-scale values includesan upper limit value of the threshold range 762 to mark the startingpoint or the endpoint of the light layer thickness. Next, the processor110 may determine whether the starting point or the endpoint belongs tothe dark layer thickness or the light layer thickness according towhether the second pixel of the two consecutive gray-scale values entersor leaves the threshold range 761 of the dark layer images 710-1 to710-4 or the threshold range 762 of the light layer images 720-1 to720-3. In addition, the method for thickness calculation in detail is asdescribed above, and it will not be repeated here.

Furthermore, the processor 110 may determine the starting point or theendpoint of the dark layer thickness or the light layer thicknessaccording to the slope value being positive or negative; however, thedisclosure is not limited thereto. Specifically, when entering from thedark layer into the light layer, the gray-scale value may increasegradually. When entering from the light layer into the dark layer, thegray-scale value may decrease gradually. That is, the processor 110 maydetermine whether the second pixel of the two consecutive pixels is thedark pixel or the light pixel according to the slope value of the twoconsecutive gray-scale values. For example, the processor 110 maydetermine a starting point 710 a of the dark layer thickness accordingto the slope value being negative, and the processor 110 may determinean endpoint 710 b of the dark layer thickness according to the slopevalue being positive. Furthermore, the processor 110 may determine astarting point 720 a of the light layer thickness according to the slopevalue being positive, and the processor 110 may determine an endpoint720 b of the light layer thickness according to the slope value beingnegative.

In summary of the above, the image analysis method and the imageanalysis system of the disclosure may automatically and rapidly measurethe thickness of each layer of the image of the multi-layer structureaccording to the set measurement line segment. Therefore, a great amountof time spent on manually measuring the thickness of each layer isreduced. Furthermore, the image analysis method and the image analysissystem of the disclosure may effectively avoid the interference of imagenoise or the influence of material impurities and accurately measure thethickness of each layer of the image of the multi-layer structure.

Although the disclosure has been described with reference to the aboveembodiments, they are not intended to limit the disclosure. It will beapparent to one of ordinary skill in the art that modifications to thedescribed embodiments may be made without departing from the spirit andthe scope of the disclosure. Accordingly, the scope of the disclosurewill be defined by the attached claims and their equivalents and not bythe above detailed descriptions.

What is claimed is:
 1. An image analysis method, comprising: obtainingan image of a multi-layer structure provided by an electron microscope,displaying the image of the multi-layer structure through a displaydevice, wherein the image of the multi-layer structure is a gray-scaleimage; setting a measurement line segment on the image of themulti-layer structure, wherein the measurement line segment extendsalong a first direction; detecting a gray-scale distribution within themeasurement line segment corresponding to the image of the multi-layerstructure along the measurement line segment, wherein the gray-scaledistribution comprises a distribution of a plurality of gray-scalevalues of a plurality of pixels within the measurement line segmentcorresponding to the image of the multi-layer structure; and analyzingthe gray-scale distribution to determine a plurality of dark layerthicknesses and a plurality of light layer thicknesses in the image ofthe multi-layer structure according to a threshold range and an intervalformed by two consecutive gray-scale values.
 2. The image analysismethod according to claim 1, wherein the image of the multi-layerstructure comprises a plurality of dark layer images and a plurality oflight layer images, the dark layer images and the light layer images arearranged in an interlacing manner along the first direction, and thedark layer images and the light layer images respectively extend along asecond direction, wherein the first direction is perpendicular to thesecond direction.
 3. The image analysis method according to claim 1,wherein analyzing the gray-scale distribution comprises comparing thegray-scale values of the pixels and the threshold range to determine aplurality of dark pixels corresponding to the dark layer thicknesses anda plurality of light pixels corresponding to the light layerthicknesses.
 4. The image analysis method according to claim 3, whereinthe threshold range comprises a first threshold range and a secondthreshold range, wherein analyzing the gray-scale distributioncomprises: in response to determining that the gray-scale values belongto the first threshold range, determining that the pixels correspondingto the gray-scale values belong to the dark pixels; and in response todetermining that the gray-scale values belong to the second thresholdrange, determining that the pixels corresponding to the gray-scalevalues belong to the light pixels.
 5. The image analysis methodaccording to claim 4, wherein the dark layer thicknesses correspond to anumber of the dark pixels of the dark layer thicknesses, and the lightlayer thicknesses correspond to a number of the light pixels of thelight layer thicknesses.
 6. The image analysis method according to claim4, wherein analyzing the gray-scale distribution comprises: determiningthat an interval formed by two consecutive gray-scale values among thegray-scale values comprises a first upper limit value of the firstthreshold range, and in response to determining that a second pixel ofthe two consecutive pixels is the dark pixel, marking the second pixelof the corresponding two consecutive pixels as a dark layer startingpoint of the corresponding dark layer thickness; and determining that aninterval formed by two consecutive gray-scale values among thegray-scale values comprises a second upper limit value of the secondthreshold range, and in response to determining that a second pixel ofthe two consecutive pixels is the light pixel, marking the second pixelof the corresponding two consecutive pixels as a light layer startingpoint of the corresponding light layer thickness.
 7. The image analysismethod according to claim 6, wherein analyzing the gray-scaledistribution comprises: determining that an interval formed by twoconsecutive gray-scale values among the gray-scale values comprises thefirst upper limit value of the first threshold range, and in response todetermining that a first pixel of the two consecutive pixels is the darkpixel, marking the first pixel of the corresponding two consecutivepixels as a dark layer endpoint of the corresponding dark layerthickness; and determining that an interval formed by two consecutivegray-scale values among the gray-scale values comprises the second upperlimit value of the second threshold range, and in response todetermining that a first pixel of the two consecutive pixels is thelight pixel, marking the first pixel of the corresponding twoconsecutive pixels as a light layer endpoint of the corresponding lightlayer thickness.
 8. The image analysis method according to claim 7,wherein the dark layer thicknesses correspond to a number of the darkpixels between the dark layer starting point and the dark layer endpointof the dark layer thicknesses, and the light layer thicknessescorrespond to a number of the light pixels between the light layerstarting point and the light layer endpoint of the light layerthicknesses.
 9. The image analysis method according to claim 8, furthercomprising: after checking that the adjacent dark layer thickness or theadjacent light layer thickness corresponds to the same pixel, combiningthe adjacent light layer thickness or the adjacent dark layer thicknessinto the single light layer thickness or the single dark layerthickness.
 10. An image analysis system, comprising: an electronmicroscope configured to provide an image of a multi-layer structure; adisplay device configured to display the image of the multi-layerstructure; and an image analysis device coupled to the electronmicroscope and the display device to obtain the image of the multi-layerstructure provided by the electron microscope and output the image ofthe multi-layer structure to the display device, wherein the imageanalysis device comprises: a storage device comprising an image analysismodule; and a processor coupled to the storage device, wherein theprocessor inputs the image of the multi-layer structure into the imageanalysis module, the processor sets a measurement line segment on theimage of the multi-layer structure, wherein the measurement line segmentextends along a first direction; the processor detects a gray-scaledistribution within the measurement line segment corresponding to theimage of the multi-layer structure along the measurement line segmentthrough the image analysis module, and the processor analyzes thegray-scale distribution through the image analysis module to determine aplurality of dark layer thicknesses and a plurality of light layerthicknesses in the image of the multi-layer structure according to athreshold range.